Interactive plotting libraries: topic introduction

In this part of the course, we will cover the following concepts:

• Discover different functions to build interactive visualizations
• Visualize data with highcharter

Warm-up

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Interactive visualizations with highcharter

• Highcharter is an R wrapper that allows R users to tap into one of the most comprehensive data visualization JavaScript-based libraries

• Though free for individual research and non-profit purposes, there are some restrictions

• You may need a license to integrate it into a software or organization-wide products

• For more information, refer to Highcharter’s website
  
  

Installing highcharter

# Install `highcharter` package.
install.packages("highcharter")

# Load the library.
library(highcharter)

# View documentation.
library(help = "highcharter")

Using highchart() function

?highchart

hchart() vs highchart()

?hchart

hchart(Some_data,       #<- dataset to use
       "plot_type",     #<- plot type to use
       hcaes(x = variable1, #<- x-axis mapping 
             y = variable2, #<- y-axis mapping 
             group = variable3, #<- group by
             ...))              

Layers in Highcharter: series

• The highcharter library has its own vocabulary
• Each new data / graphic layer in highcharter is called a series
• Series can be of different types; some common ones are listed below:

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Directory settings

• In order to maximize the efficiency of your workflow, use the box package and encode your directory structure into variables

• Let the main_dir be the variable corresponding to your materials folder

# Set `main_dir` to the location of your materials folder.

path = box::file()
main_dir = dirname(dirname(path))

Directory settings (cont’d)

# Make `data_dir` from the `main_dir` and
# remainder of the path to data directory.
data_dir = paste0(main_dir, "/data")
# Make `plots_dir` from the `main_dir` and
# remainder of the path to plots directory.
plot_dir = paste0(main_dir, "/plots")

Introducing HDS data set

• We will begin by exploring a dataset called healthcare-dataset-stroke-data

• This dataset contains information about age, gender, hypertension, bmi, and other parameters to know the chances of getting a stroke

• The goal is to understand how different variables in the dataset affect the chances of a person suffering from a stroke

• The dataset has 12 characteristics (columns), of which:

  • 10 columns relate to the quality and characteristics of the life of different people
  • The stroke column represents whether the people had a stroke or not

Load HDS dataset

# Read CSV file called "healthcare-dataset-stroke-data.csv"
HDS = read.csv(file = file.path(data_dir,"/healthcare-dataset-stroke-data.csv"), #<- provide file path
               header = TRUE,            #<- if file has header set to TRUE
               stringsAsFactors = FALSE) #<- read strings as characters, not as factors

Subset data

Prepare Data

• But before sub-setting the data, let’s handle the missing data in the dataset
• Then convert bmi into a numeric column followed by imputing the missing values with the mean

HDS$bmi <- as.numeric(as.character(HDS$bmi)) ##converting bmi column to numeric
# NA imputation
# we can use is.na() function to know about NA values
HDS$bmi[is.na(HDS$bmi)]<-mean(HDS$bmi,na.rm=TRUE) # Replacing na values of bmi column with it's mean bmi

Prepare Data

• Let’s tidy the data and transform it from a wide to a long format
• This will be especially useful later for univariate visualizations

library(tidyverse)

# Now Let's make a vector of column indices we would like to save.

column_ids = select(HDS, age,bmi,avg_glucose_level,stroke)
HDS_subset = column_ids

Create a subset

• Now let’s create a different subset to help us build a scatterplot and inspect the head

# Prep data for scatterplot

HDS_subset_long = HDS_subset %>%
  gather(-age, #<- gather all variables but `age`
         key = "variable",                            
         value = "value") %>%                         
  # All other transformations we've done before.
  group_by(variable) %>%
  mutate(norm_value = value/mean(value, na.rm = TRUE))

head(HDS_subset_long)

# A tibble: 6 x 4
# Groups:   variable [1]
    age variable value norm_value
  <dbl> <chr>    <dbl>      <dbl>
1    67 bmi       36.6      1.27 
2    61 bmi       28.9      1    
3    80 bmi       32.5      1.12 
4    49 bmi       34.4      1.19 
5    79 bmi       24        0.831
6    81 bmi       29        1.00 

Construct a scatterplot using hchart

• To construct a scatterplot, we use the hchart() function and pass the data, plot type (scatter), and aesthetics to it as arguments

# Construct an interactive scatterplot.
scatter_interactive =              #<- name the plot   
  hchart(HDS_subset_long,         #<- set data
         "scatter",                #<- plot type "scatter"
          hcaes(x = norm_value,    #<- set aesthetics to map x-axis
                y = age,         #<- set aesthetics to map y-axis
                group = variable)) #<- group by

• In R, interactive charts appear in the Viewer pane, right next to the Help tab

Construct a scatterplot using hchart (cont’d)

scatter_interactive

Selecting categories

• Every plotted category seen in the legend is a series in highcharter
• When hchart() detects more than one category, it auto-colors by series
• We can interactively select and de-select which series to display by clicking on the series names in the legend

Customizing plots with the pipe operator

# Pipe chart options to original chart.

scatter_interactive = scatter_interactive %>%
  # Use chart options to specify zoom.
  hc_chart(zoomType = "xy") 

scatter_interactive

Adding a title

# Pipe chart options to original chart.
scatter_interactive = scatter_interactive %>%
 # Add title to the plot.
 hc_title(text = "HDS data: Age vs. other variables")

scatter_interactive

Knowledge check

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Exercise

Module completion checklist

Interactive plotting libraries: topic summary

In this part of the course, we have covered:

• Discovering different functions to build interactive visualization
• Visualizing data with highcharter

Congratulations on completing this module!